Going Native on Google Cloud: Built-in migration tools and strategies

You plan a straight migration. Week three, a “harmless” service pulls five others with it, and suddenly the schedule slips. Sound familiar?

Most companies going through cloud migration face similar challenges. Often enough, years of patchwork code, hidden dependencies, and forgotten data pipelines don’t reveal themselves until the migration is already in motion.

No wonder, according to research by McKinsey & Company, 62% of migrations miss expectations, costs rise by an average of 14%, and 38% slip by at least a quarter. So, what’s complicating the migration process? It isn’t cloud complexity; it is a lack of visibility, with problems only surfacing after you’ve committed to the move.

Different cloud platforms try to solve this in different ways. What gives Google Cloud Platform (GCP) an edge is its emphasis on early discovery. Its native tools are designed to expose connections, map dependencies, and highlight risks before you start the actual move.

This blog explores how to use those core capabilities within Google Cloud, and how you can ultimately supercharge them with modern automation to migrate with fewer surprises and far less drama.

Choosing the right cloud migration path

Before you start deploying resources in Google Cloud, it’s essential to clarify why you’re migrating. The strategy you choose affects effort, cost, and long-term outcomes. Here are the most common migration approaches and what they mean in real-world practice:

Rehost (Lift-and-Shift): Move applications to the cloud as-is. This is the fastest way to exit a data centre, but it doesn’t immediately unlock cloud-native benefits

Retain and Retire: Keep compliance-heavy or tightly regulated workloads on-premises while retiring legacy applications that no longer serve a purpose

Replatform (Lift, Tinker, and Shift): Make small changes like replacing a self-managed database with a managed cloud database to gain efficiency without rewriting the application

Refactor (Re-architect): Rewrite or significantly restructure an application to fully leverage cloud-native capabilities, such as transforming a monolith into microservices

Cloud migration assessment: The classic challenges

You can’t migrate what you don’t know exists, and most legacy environments are poorly documented. If you attempt manual dependency mapping, you’ll end up buried in spreadsheets for months, relying on the tribal knowledge of a few veteran engineers

Google Cloud’s Migration Center solves the baseline of this problem. It acts as a centralized hub for the entire migration lifecycle, eliminating the need for multiple discovery tools. GCP automates asset discovery across on-prem and multi-cloud environments, maps server-level application dependencies, and generates detailed Total Cost of Ownership (TCO) estimates. This gives you a clear, data-driven business case before you migrate, showing what your current infrastructure will cost when mapped to GCP’s compute and storage tiers

However, even with tools like Migration Center, large enterprises face immense complexity. Standard tools map infrastructure, but engineers still have to manually dig into the code to figure out why two servers are talking, or guess which old library might break during the move

Executing the migration: Using GCP’s native tools

Once your assessment and manual planning are complete, Google Cloud provides a robust set of native tools to move your workloads safely and efficiently

Migrating Infrastructure

• Migrate to virtual machines: Perfect for rehosting. Point it at your VMware or AWS environment, and it streams your workloads directly into Compute Engine while keeping downtime minimal during final cutover

• Migrate to Containers: Ideal when you want light modernization. This tool extracts applications from traditional VMs and packages them into containers for Google Kubernetes Engine (GKE) or Cloud Run. Testing is essential, but it’s a powerful bridge for teams aiming to containerize without a total rewrite

Data migration: The most critical step

Infrastructure is easy; data is where the real anxiety lives. Databases carry the operational gravity; if something breaks, everybody feels it

• Database Migration Service (DMS): For standard SQL databases, DMS uses Change Data Capture (CDC) to keep on-premises and cloud databases in sync in real time. You can test safely, validate thoroughly, and switch over only when confident

• Storage Transfer Service & Transfer Appliance: Use the Storage Transfer Service for large online file transfers. If you’re migrating petabytes and network bandwidth isn’t enough, Google ships a physical Transfer Appliance. Load the data locally, send it back, and Google plugs it directly into their backbone

Hard-learned cloud migration best practices

If I could give a migration team only three pieces of advice, they would be:

• Automate discovery: Relying on memory guarantees you’ll miss something critical.

• Move the database first; applications follow the data: Get replication running, stabilize it, and then shift the application tier.

• Start with a low-risk workload: Don’t migrate your mission-critical billing system first. Pick an internal tool to build confidence and let the team learn the tooling.

The next level: Overcoming classic complexity with Agentic AI

While Google Cloud’s native tools provide an excellent foundation, massive legacy estates require deep analysis that traditional scanning and human architects simply cannot scale efficiently. This is where traditional approaches hit a bottleneck, and where system integrators step in with advanced tooling.

At Nagarro, we bridge this gap using ATLAS, our agentic AI-powered cloud service delivery platform.

How Agentic AI outperforms classic manual assessment?

Unlike static scripts or standard infrastructure scanners, agentic systems operate like autonomous assistants.

• Eliminating manual code analysis: Instead of engineers spending weeks manually tracing application dependencies and legacy libraries, Atlas deploys AI agents that autonomously analyze the actual codebase.

• Proactive risk identification: Classic tools tell you what servers you have; Atlas tells you what will break when you move them. It identifies modernization risks and flags migration blockers long before they become problems.

• Accelerated Implementation: Because the AI interprets the data and connects the dots at the code level, the manual effort required for planning is drastically reduced. It practically eliminates the "blind spots" that typically cause migrations to slip off schedule.

Cloud migrations will always involve complexity, but they don’t need to be chaotic. By establishing a strong foundation with GCP's native tooling and layering on the sheer speed and analytical power of agentic AI like Nagarro's ATLAS, you can graduate from heavy, manual assessments to a highly predictable, risk-free cloud modernization.